{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 - Attention is All You Need\n",
    "\n",
    "In this notebook we will be implementing a (slightly modified version) of the Transformer model from the [Attention is All You Need](https://arxiv.org/abs/1706.03762) paper. All images in this notebook will be taken from the Transformer paper. For more information about the Transformer, [see](https://www.mihaileric.com/posts/transformers-attention-in-disguise/) [these](https://jalammar.github.io/illustrated-transformer/) [three](http://nlp.seas.harvard.edu/2018/04/03/attention.html) articles.\n",
    "\n",
    "![](assets/transformer1.png)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Similar to the Convolutional Sequence-to-Sequence model, the Transformer does not use any recurrence. It also does not use any convolutional layers. Instead the model is entirely made up of linear layers, attention mechanisms and normalization. \n",
    "\n",
    "As of January 2020, Transformers are the dominant architecture in NLP and are used to achieve state-of-the-art results for many tasks and it appears as if they will be for the near future. \n",
    "\n",
    "The most popular Transformer variant is [BERT](https://arxiv.org/abs/1810.04805) (**B**idirectional **E**ncoder **R**epresentations from **T**ransformers) and pre-trained versions of BERT are commonly used to replace the embedding layers - if not more - in NLP models. \n",
    "\n",
    "A common library used when dealing with pre-trained transformers is the [Transformers](https://huggingface.co/transformers/) library, see [here](https://huggingface.co/transformers/pretrained_models.html) for a list of all pre-trained models available.\n",
    "\n",
    "The differences between the implementation in this notebook and the paper are:\n",
    "- we use a learned positional encoding instead of a static one\n",
    "- we use the standard Adam optimizer with a static learning rate instead of one with warm-up and cool-down steps\n",
    "- we do not use label smoothing\n",
    "\n",
    "We make all of these changes as they closely follow BERT's set-up and the majority of Transformer variants use a similar set-up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data\n",
    "\n",
    "As always, let's import all the required modules and set the random seeds for reproducability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import torchtext\n",
    "from torchtext.datasets import Multi30k\n",
    "from torchtext.data import Field, BucketIterator\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "import spacy\n",
    "import numpy as np\n",
    "\n",
    "import random\n",
    "import math\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll then create our tokenizers as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "spacy_de = spacy.load('de')\n",
    "spacy_en = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_de(text):\n",
    "    \"\"\"\n",
    "    Tokenizes German text from a string into a list of strings\n",
    "    \"\"\"\n",
    "    return [tok.text for tok in spacy_de.tokenizer(text)]\n",
    "\n",
    "def tokenize_en(text):\n",
    "    \"\"\"\n",
    "    Tokenizes English text from a string into a list of strings\n",
    "    \"\"\"\n",
    "    return [tok.text for tok in spacy_en.tokenizer(text)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our fields are the same as the previous notebook. The model expects data to be fed in with the batch dimension first, so we use `batch_first = True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC = Field(tokenize = tokenize_de, \n",
    "            init_token = '<sos>', \n",
    "            eos_token = '<eos>', \n",
    "            lower = True, \n",
    "            batch_first = True)\n",
    "\n",
    "TRG = Field(tokenize = tokenize_en, \n",
    "            init_token = '<sos>', \n",
    "            eos_token = '<eos>', \n",
    "            lower = True, \n",
    "            batch_first = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then load the Multi30k dataset and build the vocabulary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, valid_data, test_data = Multi30k.splits(exts = ('.de', '.en'), \n",
    "                                                    fields = (SRC, TRG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC.build_vocab(train_data, min_freq = 2)\n",
    "TRG.build_vocab(train_data, min_freq = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define the device and the data iterator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128\n",
    "\n",
    "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n",
    "    (train_data, valid_data, test_data), \n",
    "     batch_size = BATCH_SIZE,\n",
    "     device = device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Model\n",
    "\n",
    "Next, we'll build the model. Like previous notebooks it is made up of an *encoder* and a *decoder*, with the encoder *encoding* the input/source sentence (in German) into *context vector* and the decoder then *decoding* this context vector to output our output/target sentence (in English). \n",
    "\n",
    "### Encoder\n",
    "\n",
    "Similar to the ConvSeq2Seq model, the Transformer's encoder does not attempt to compress the entire source sentence, $X = (x_1, ... ,x_n)$, into a single context vector, $z$. Instead it produces a sequence of context vectors, $Z = (z_1, ... , z_n)$. So, if our input sequence was 5 tokens long we would have $Z = (z_1, z_2, z_3, z_4, z_5)$. Why do we call this a sequence of context vectors and not a sequence of hidden states? A hidden state at time $t$ in an RNN has only seen tokens $x_t$ and all the tokens before it. However, each context vector here has seen all tokens at all positions within the input sequence.\n",
    "\n",
    "![](assets/transformer-encoder.png)\n",
    "\n",
    "First, the tokens are passed through a standard embedding layer. Next, as the model has no recurrent it has no idea about the order of the tokens within the sequence. We solve this by using a second embedding layer called a *positional embedding layer*. This is a standard embedding layer where the input is not the token itself but the position of the token within the sequence, starting with the first token, the `<sos>` (start of sequence) token, in position 0. The position embedding has a \"vocabulary\" size of 100, which means our model can accept sentences up to 100 tokens long. This can be increased if we want to handle longer sentences.\n",
    "\n",
    "The original Transformer implementation from the Attention is All You Need paper does not learn positional embeddings. Instead it uses a fixed static embedding. Modern Transformer architectures, like BERT, use positional embeddings instead, hence we have decided to use them in these tutorials. Check out [this](http://nlp.seas.harvard.edu/2018/04/03/attention.html#positional-encoding) section to read more about the positional embeddings used in the original Transformer model.\n",
    "\n",
    "Next, the token and positional embeddings are elementwise summed together to get a vector which contains information about the token and also its position with in the sequence. However, before they are summed, the token embeddings are multiplied by a scaling factor which is $\\sqrt{d_{model}}$, where $d_{model}$ is the hidden dimension size, `hid_dim`. This supposedly reduces variance in the embeddings and the model is difficult to train reliably without this scaling factor. Dropout is then applied to the combined embeddings.\n",
    "\n",
    "The combined embeddings are then passed through $N$ *encoder layers* to get $Z$, which is then output and can be used by the decoder.\n",
    "\n",
    "The source mask, `src_mask`, is simply the same shape as the source sentence but has a value of 1 when the token in the source sentence is not a `<pad>` token and 0 when it is a `<pad>` token. This is used in the encoder layers to mask the multi-head attention mechanisms, which are used to calculate and apply attention over the source sentence, so the model does not pay attention to `<pad>` tokens, which contain no useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, \n",
    "                 input_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim,\n",
    "                 dropout, \n",
    "                 device,\n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "\n",
    "        self.device = device\n",
    "        \n",
    "        self.tok_embedding = nn.Embedding(input_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.ModuleList([EncoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim,\n",
    "                                                  dropout, \n",
    "                                                  device) \n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
    "        \n",
    "    def forward(self, src, src_mask):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #src_mask = [batch size, src len]\n",
    "        \n",
    "        batch_size = src.shape[0]\n",
    "        src_len = src.shape[1]\n",
    "        \n",
    "        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "        \n",
    "        #pos = [batch size, src len]\n",
    "        \n",
    "        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            src = layer(src, src_mask)\n",
    "            \n",
    "        #src = [batch size, src len, hid dim]\n",
    "            \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoder Layer\n",
    "\n",
    "The encoder layers are where all of the \"meat\" of the encoder is contained. We first pass the source sentence and its mask into the *multi-head attention layer*, then perform dropout on it, apply a residual connection and pass it through a [Layer Normalization](https://arxiv.org/abs/1607.06450) layer. We then pass it through a *position-wise feedforward* layer and then, again, apply dropout, a residual connection and then layer normalization to get the output of this layer which is fed into the next layer. The parameters are not shared between layers. \n",
    "\n",
    "The mutli head attention layer is used by the encoder layer to attend to the source sentence, i.e. it is calculating and applying attention over itself instead of another sequence, hence we call it *self attention*.\n",
    "\n",
    "[This](https://mlexplained.com/2018/01/13/weight-normalization-and-layer-normalization-explained-normalization-in-deep-learning-part-2/) article goes into more detail about layer normalization, but the gist is that it normalizes the values of the features, i.e. across the hidden dimension, so each feature has a mean of 0 and a standard deviation of 1. This allows neural networks with a larger number of layers, like the Transformer, to be trained easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Module):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim,  \n",
    "                 dropout, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.ff_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, src, src_mask):\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        #src_mask = [batch size, src len]\n",
    "                \n",
    "        #self attention\n",
    "        _src, _ = self.self_attention(src, src, src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        src = self.self_attn_layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _src = self.positionwise_feedforward(src)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        src = self.ff_layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutli Head Attention Layer\n",
    "\n",
    "One of the key, novel concepts introduced by the Transformer paper is the *multi-head attention layer*. \n",
    "\n",
    "![](assets/transformer-attention.png)\n",
    "\n",
    "Attention can be though of as *queries*, *keys* and *values* - where the query is used with the key to get an attention vector (usually the output of a *softmax* operation and has all values between 0 and 1 which sum to 1) which is then used to get a weighted sum of the values.\n",
    "\n",
    "The Transformer uses *scaled dot-product attention*, where the query and key are combined by taking the dot product between them, then applying the softmax operation and scaling by $d_k$ before finally then multiplying by the value. $d_k$ is the *head dimension*, `head_dim`, which we will shortly explain further.\n",
    "\n",
    "$$ \\text{Attention}(Q, K, V) = \\text{Softmax} \\big( \\frac{QK^T}{\\sqrt{d_k}} \\big)V $$ \n",
    "\n",
    "This is similar to standard *dot product attention* but is scaled by $d_k$, which the paper states is used to stop the results of the dot products growing large, causing gradients to become too small.\n",
    "\n",
    "However, the scaled dot-product attention isn't simply applied to the queries, keys and values. Instead of doing a single attention application the queries, keys and values have their `hid_dim` split into $h$ *heads* and the scaled dot-product attention is calculated over all heads in parallel. This means instead of paying attention to one concept per attention application, we pay attention to $h$. We then re-combine the heads into their `hid_dim` shape, thus each `hid_dim` is potentially paying attention to $h$ different concepts.\n",
    "\n",
    "$$ \\text{MultiHead}(Q, K, V) = \\text{Concat}(\\text{head}_1,...,\\text{head}_h)W^O $$\n",
    "\n",
    "$$\\text{head}_i = \\text{Attention}(QW_i^Q, KW_i^K, VW_i^V) $$\n",
    "\n",
    "$W^O$ is the linear layer applied at the end of the multi-head attention layer, `fc`. $W^Q, W^K, W^V$ are the linear layers `fc_q`, `fc_k` and `fc_v`.\n",
    "\n",
    "Walking through the module, first we calculate $QW^Q$, $KW^K$ and $VW^V$ with the linear layers, `fc_q`, `fc_k` and `fc_v`, to give us `Q`, `K` and `V`. Next, we split the `hid_dim` of the query, key and value into `n_heads` using `.view` and correctly permute them so they can be multiplied together. We then calculate the `energy` (the un-normalized attention) by multiplying `Q` and `K` together and scaling it by the square root of `head_dim`, which is calulated as `hid_dim // n_heads`. We then mask the energy so we do not pay attention over any elements of the sequeuence we shouldn't, then apply the softmax and dropout. We then apply the attention to the value heads, `V`, before combining the `n_heads` together. Finally, we multiply this $W^O$, represented by `fc_o`. \n",
    "\n",
    "Note that in our implementation the lengths of the keys and values are always the same, thus when matrix multiplying the output of the softmax, `attention`, with `V` we will always have valid dimension sizes for matrix multiplication. This multiplication is carried out using `torch.matmul` which, when both tensors are >2-dimensional, does a batched matrix multiplication over the last two dimensions of each tensor. This will be a **[query len, key len] x [value len, head dim]** batched matrix multiplication over the batch size and each head which provides the **[batch size, n heads, query len, head dim]** result.\n",
    "\n",
    "One thing that looks strange at first is that dropout is applied directly to the attention. This means that our attention vector will most probably not sum to 1 and we may pay full attention to a token but the attention over that token is set to 0 by dropout. This is never explained, or even mentioned, in the paper however is used by the [official implementation](https://github.com/tensorflow/tensor2tensor/) and every Transformer implementation since, [including BERT](https://github.com/google-research/bert/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttentionLayer(nn.Module):\n",
    "    def __init__(self, hid_dim, n_heads, dropout, device):\n",
    "        super().__init__()\n",
    "        \n",
    "        assert hid_dim % n_heads == 0\n",
    "        \n",
    "        self.hid_dim = hid_dim\n",
    "        self.n_heads = n_heads\n",
    "        self.head_dim = hid_dim // n_heads\n",
    "        \n",
    "        self.fc_q = nn.Linear(hid_dim, hid_dim)\n",
    "        self.fc_k = nn.Linear(hid_dim, hid_dim)\n",
    "        self.fc_v = nn.Linear(hid_dim, hid_dim)\n",
    "        \n",
    "        self.fc_o = nn.Linear(hid_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)\n",
    "        \n",
    "    def forward(self, query, key, value, mask = None):\n",
    "        \n",
    "        batch_size = query.shape[0]\n",
    "        \n",
    "        #query = [batch size, query len, hid dim]\n",
    "        #key = [batch size, key len, hid dim]\n",
    "        #value = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = self.fc_q(query)\n",
    "        K = self.fc_k(key)\n",
    "        V = self.fc_v(value)\n",
    "        \n",
    "        #Q = [batch size, query len, hid dim]\n",
    "        #K = [batch size, key len, hid dim]\n",
    "        #V = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        \n",
    "        #Q = [batch size, n heads, query len, head dim]\n",
    "        #K = [batch size, n heads, key len, head dim]\n",
    "        #V = [batch size, n heads, value len, head dim]\n",
    "                \n",
    "        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale\n",
    "        \n",
    "        #energy = [batch size, n heads, query len, key len]\n",
    "        \n",
    "        if mask is not None:\n",
    "            energy = energy.masked_fill(mask == 0, -1e10)\n",
    "        \n",
    "        attention = torch.softmax(energy, dim = -1)\n",
    "                \n",
    "        #attention = [batch size, n heads, query len, key len]\n",
    "                \n",
    "        x = torch.matmul(self.dropout(attention), V)\n",
    "        \n",
    "        #x = [batch size, n heads, query len, head dim]\n",
    "        \n",
    "        x = x.permute(0, 2, 1, 3).contiguous()\n",
    "        \n",
    "        #x = [batch size, query len, n heads, head dim]\n",
    "        \n",
    "        x = x.view(batch_size, -1, self.hid_dim)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        x = self.fc_o(x)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        return x, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Position-wise Feedforward Layer\n",
    "\n",
    "The other main block inside the encoder layer is the *position-wise feedforward layer* This is relatively simple compared to the multi-head attention layer. The input is transformed from `hid_dim` to `pf_dim`, where `pf_dim` is usually a lot larger than `hid_dim`. The original Transformer used a `hid_dim` of 512 and a `pf_dim` of 2048. The ReLU activation function and dropout are applied before it is transformed back into a `hid_dim` representation. \n",
    "\n",
    "Why is this used? Unfortunately, it is never explained in the paper.\n",
    "\n",
    "BERT uses the [GELU](https://arxiv.org/abs/1606.08415) activation function, which can be used by simply switching `torch.relu` for `F.gelu`. Why did they use GELU? Again, it is never explained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionwiseFeedforwardLayer(nn.Module):\n",
    "    def __init__(self, hid_dim, pf_dim, dropout):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.fc_1 = nn.Linear(hid_dim, pf_dim)\n",
    "        self.fc_2 = nn.Linear(pf_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        x = self.dropout(torch.relu(self.fc_1(x)))\n",
    "        \n",
    "        #x = [batch size, seq len, pf dim]\n",
    "        \n",
    "        x = self.fc_2(x)\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder\n",
    "\n",
    "The objective of the decoder is to take the encoded representation of the source sentence, $Z$, and convert it into predicted tokens in the target sentence, $\\hat{Y}$. We then compare $\\hat{Y}$ with the actual tokens in the target sentence, $Y$, to calculate our loss, which will be used to calculate the gradients of our parameters and then use our optimizer to update our weights in order to improve our predictions. \n",
    "\n",
    "![](assets/transformer-decoder.png)\n",
    "\n",
    "The decoder is similar to encoder, however it now has two multi-head attention layers. A *masked multi-head attention layer* over the target sequence, and a multi-head attention layer which uses the decoder representation as the query and the encoder representation as the key and value.\n",
    "\n",
    "The decoder uses positional embeddings and combines - via an elementwise sum - them with the scaled embedded target tokens, followed by dropout. Again, our positional encodings have a \"vocabulary\" of 100, which means they can accept sequences up to 100 tokens long. This can be increased if desired.\n",
    "\n",
    "The combined embeddings are then passed through the $N$ decoder layers, along with the encoded source, `enc_src`, and the source and target masks. Note that the number of layers in the encoder does not have to be equal to the number of layers in the decoder, even though they are both denoted by $N$.\n",
    "\n",
    "The decoder representation after the $N^{th}$ layer is then passed through a linear layer, `fc_out`. In PyTorch, the softmax operation is contained within our loss function, so we do not explicitly need to use a softmax layer here.\n",
    "\n",
    "As well as using the source mask, as we did in the encoder to prevent our model attending to `<pad>` tokens, we also use a target mask. This will be explained further in the `Seq2Seq` model which encapsulates both the encoder and decoder, but the gist of it is that it performs a similar operation as the decoder padding in the convolutional sequence-to-sequence model. As we are processing all of the target tokens at once in parallel we need a method of stopping the decoder from \"cheating\" by simply \"looking\" at what the next token in the target sequence is and outputting it. \n",
    "\n",
    "Our decoder layer also outputs the normalized attention values so we can later plot them to see what our model is actually paying attention to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    def __init__(self, \n",
    "                 output_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 device,\n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.device = device\n",
    "        \n",
    "        self.tok_embedding = nn.Embedding(output_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.ModuleList([DecoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim, \n",
    "                                                  dropout, \n",
    "                                                  device)\n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.fc_out = nn.Linear(hid_dim, output_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
    "        \n",
    "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, trg len]\n",
    "        #src_mask = [batch size, src len]\n",
    "                \n",
    "        batch_size = trg.shape[0]\n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "                            \n",
    "        #pos = [batch size, trg len]\n",
    "            \n",
    "        trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))\n",
    "                \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            trg, attention = layer(trg, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        output = self.fc_out(trg)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "            \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder Layer\n",
    "\n",
    "As mentioned previously, the decoder layer is similar to the encoder layer except that it now has two multi-head attention layers, `self_attention` and `encoder_attention`. \n",
    "\n",
    "The first performs self-attention, as in the encoder, by using the decoder representation so far as the query, key and value. This is followed by dropout, residual connection and layer normalization. This `self_attention` layer uses the target sequence mask, `trg_mask`, in order to prevent the decoder from \"cheating\" by paying attention to tokens that are \"ahead\" of the one it is currently processing as it processes all tokens in the target sentence in parallel.\n",
    "\n",
    "The second is how we actually feed the encoded source sentence, `enc_src`, into our decoder. In this multi-head attention layer the queries are the decoder representations and the keys and values are the decoder representations. Here, the source mask, `src_mask` is used to prevent the multi-head attention layer from attending to `<pad>` tokens within the source sentence. This is then followed by the dropout, residual connection and layer normalization layers. \n",
    "\n",
    "Finally, we pass this through the position-wise feedforward layer and yet another sequence of dropout, residual connection and layer normalization.\n",
    "\n",
    "The decoder layer isn't introducing any new concepts, just using the same set of layers as the encoder in a slightly different way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Module):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.ff_layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, trg len]\n",
    "        #src_mask = [batch size, src len]\n",
    "        \n",
    "        #self attention\n",
    "        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))\n",
    "            \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "            \n",
    "        #encoder attention\n",
    "        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))\n",
    "                    \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _trg = self.positionwise_feedforward(trg)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        trg = self.ff_layer_norm(trg + self.dropout(_trg))\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return trg, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seq2Seq\n",
    "\n",
    "Finally, we have the `Seq2Seq` module which encapsulates the encoder and decoder, as well as handling the creation of the masks.\n",
    "\n",
    "The source mask is created by checking where the source sequence is not equal to a `<pad>` token. It is 1 where the token is not a `<pad>` token and 0 when it is. It is then unsqueezed so it can be correctly broadcast when applying the mask to the `energy`, which of shape **_[batch size, n heads, seq len, seq len]_**.\n",
    "\n",
    "The target mask is slightly more complicated. First, we create a mask for the `<pad>` tokens, as we did for the source mask. Next, we create a \"subsequent\" mask, `trg_sub_mask`, using `torch.tril`. This creates a diagonal matrix where the elements above the diagonal will be zero and the elements below the diagonal will be set to whatever the input tensor is. In this case, the input tensor will be a tensor filled with ones. So this means our `trg_sub_mask` will look something like this (for a target with 5 tokens):\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 1\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "This shows what each target token (row) is allowed to look at (column). The first target token has a mask of **_[1, 0, 0, 0, 0]_** which means it can only look at the first target token. The second target token has a mask of **_[1, 1, 0, 0, 0]_** which it means it can look at both the first and second target tokens. \n",
    "\n",
    "The \"subsequent\" mask is then logically anded with the padding mask, this combines the two masks ensuring both the subsequent tokens and the padding tokens cannot be attended to. For example if the last two tokens were `<pad>` tokens the mask would look like:\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "After the masks are created, they used with the encoder and decoder along with the source and target sentences to get our predicted target sentence, `output`, along with the decoder's attention over the source sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Seq2Seq(nn.Module):\n",
    "    def __init__(self, \n",
    "                 encoder, \n",
    "                 decoder, \n",
    "                 src_pad_idx, \n",
    "                 trg_pad_idx, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.src_pad_idx = src_pad_idx\n",
    "        self.trg_pad_idx = trg_pad_idx\n",
    "        self.device = device\n",
    "        \n",
    "    def make_src_mask(self, src):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        \n",
    "        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "\n",
    "        return src_mask\n",
    "    \n",
    "    def make_trg_mask(self, trg):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        \n",
    "        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "        \n",
    "        #trg_pad_mask = [batch size, 1, 1, trg len]\n",
    "        \n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device = self.device)).bool()\n",
    "        \n",
    "        #trg_sub_mask = [trg len, trg len]\n",
    "            \n",
    "        trg_mask = trg_pad_mask & trg_sub_mask\n",
    "        \n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        return trg_mask\n",
    "\n",
    "    def forward(self, src, trg):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #trg = [batch size, trg len]\n",
    "                \n",
    "        src_mask = self.make_src_mask(src)\n",
    "        trg_mask = self.make_trg_mask(trg)\n",
    "        \n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        enc_src = self.encoder(src, src_mask)\n",
    "        \n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "                \n",
    "        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Seq2Seq Model\n",
    "\n",
    "We can now define our encoder and decoders. This model is significantly smaller than Transformers used in research today, but is able to be run on a single GPU quickly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = len(SRC.vocab)\n",
    "OUTPUT_DIM = len(TRG.vocab)\n",
    "HID_DIM = 256\n",
    "ENC_LAYERS = 3\n",
    "DEC_LAYERS = 3\n",
    "ENC_HEADS = 8\n",
    "DEC_HEADS = 8\n",
    "ENC_PF_DIM = 512\n",
    "DEC_PF_DIM = 512\n",
    "ENC_DROPOUT = 0.1\n",
    "DEC_DROPOUT = 0.1\n",
    "\n",
    "enc = Encoder(INPUT_DIM, \n",
    "              HID_DIM, \n",
    "              ENC_LAYERS, \n",
    "              ENC_HEADS, \n",
    "              ENC_PF_DIM, \n",
    "              ENC_DROPOUT, \n",
    "              device)\n",
    "\n",
    "dec = Decoder(OUTPUT_DIM, \n",
    "              HID_DIM, \n",
    "              DEC_LAYERS, \n",
    "              DEC_HEADS, \n",
    "              DEC_PF_DIM, \n",
    "              DEC_DROPOUT, \n",
    "              device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, use them to define our whole sequence-to-sequence encapsulating model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC_PAD_IDX = SRC.vocab.stoi[SRC.pad_token]\n",
    "TRG_PAD_IDX = TRG.vocab.stoi[TRG.pad_token]\n",
    "\n",
    "model = Seq2Seq(enc, dec, SRC_PAD_IDX, TRG_PAD_IDX, device).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the number of parameters, noticing it is significantly less than the 37M for the convolutional sequence-to-sequence model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 9,038,853 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The paper does not mention which weight initialization scheme was used, however Xavier uniform seems to be common amongst Transformer models, so we use it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_weights(m):\n",
    "    if hasattr(m, 'weight') and m.weight.dim() > 1:\n",
    "        nn.init.xavier_uniform_(m.weight.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.apply(initialize_weights);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimizer used in the original Transformer paper uses Adam with a learning rate that has a \"warm-up\" and then a \"cool-down\" period. BERT and other Transformer models use Adam with a fixed learning rate, so we will implement that. Check [this](http://nlp.seas.harvard.edu/2018/04/03/attention.html#optimizer) link for more details about the original Transformer's learning rate schedule.\n",
    "\n",
    "Note that the learning rate needs to be lower than the default used by Adam or else learning is unstable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.0005\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define our loss function, making sure to ignore losses calculated over `<pad>` tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss(ignore_index = TRG_PAD_IDX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll define our training loop. This is the exact same as the one used in the previous tutorial.\n",
    "\n",
    "As we want our model to predict the `<eos>` token but not have it be an input into our model we simply slice the `<eos>` token off the end of the sequence. Thus:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{trg} &= [sos, x_1, x_2, x_3, eos]\\\\\n",
    "\\text{trg[:-1]} &= [sos, x_1, x_2, x_3]\n",
    "\\end{align*}$$\n",
    "\n",
    "$x_i$ denotes actual target sequence element. We then feed this into the model to get a predicted sequence that should hopefully predict the `<eos>` token:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "$y_i$ denotes predicted target sequence element. We then calculate our loss using the original `trg` tensor with the `<sos>` token sliced off the front, leaving the `<eos>` token:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\\\\\n",
    "\\text{trg[1:]} &= [x_1, x_2, x_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "We then calculate our losses and update our parameters as is standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, iterator, optimizer, criterion, clip):\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    \n",
    "    for i, batch in enumerate(iterator):\n",
    "        \n",
    "        src = batch.src\n",
    "        trg = batch.trg\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        output, _ = model(src, trg[:,:-1])\n",
    "                \n",
    "        #output = [batch size, trg len - 1, output dim]\n",
    "        #trg = [batch size, trg len]\n",
    "            \n",
    "        output_dim = output.shape[-1]\n",
    "            \n",
    "        output = output.contiguous().view(-1, output_dim)\n",
    "        trg = trg[:,1:].contiguous().view(-1)\n",
    "                \n",
    "        #output = [batch size * trg len - 1, output dim]\n",
    "        #trg = [batch size * trg len - 1]\n",
    "            \n",
    "        loss = criterion(output, trg)\n",
    "        \n",
    "        loss.backward()\n",
    "        \n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The evaluation loop is the same as the training loop, just without the gradient calculations and parameter updates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, iterator, criterion):\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "    \n",
    "        for i, batch in enumerate(iterator):\n",
    "\n",
    "            src = batch.src\n",
    "            trg = batch.trg\n",
    "\n",
    "            output, _ = model(src, trg[:,:-1])\n",
    "            \n",
    "            #output = [batch size, trg len - 1, output dim]\n",
    "            #trg = [batch size, trg len]\n",
    "            \n",
    "            output_dim = output.shape[-1]\n",
    "            \n",
    "            output = output.contiguous().view(-1, output_dim)\n",
    "            trg = trg[:,1:].contiguous().view(-1)\n",
    "            \n",
    "            #output = [batch size * trg len - 1, output dim]\n",
    "            #trg = [batch size * trg len - 1]\n",
    "            \n",
    "            loss = criterion(output, trg)\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define a small function that we can use to tell us how long an epoch takes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we train our actual model. This model is almost 3x faster than the convolutional sequence-to-sequence model and also achieves a lower validation perplexity!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Time: 0m 11s\n",
      "\tTrain Loss: 4.226 | Train PPL:  68.454\n",
      "\t Val. Loss: 3.046 |  Val. PPL:  21.031\n",
      "Epoch: 02 | Time: 0m 11s\n",
      "\tTrain Loss: 2.824 | Train PPL:  16.837\n",
      "\t Val. Loss: 2.311 |  Val. PPL:  10.083\n",
      "Epoch: 03 | Time: 0m 11s\n",
      "\tTrain Loss: 2.239 | Train PPL:   9.387\n",
      "\t Val. Loss: 1.993 |  Val. PPL:   7.341\n",
      "Epoch: 04 | Time: 0m 11s\n",
      "\tTrain Loss: 1.886 | Train PPL:   6.594\n",
      "\t Val. Loss: 1.814 |  Val. PPL:   6.136\n",
      "Epoch: 05 | Time: 0m 11s\n",
      "\tTrain Loss: 1.640 | Train PPL:   5.156\n",
      "\t Val. Loss: 1.709 |  Val. PPL:   5.522\n",
      "Epoch: 06 | Time: 0m 11s\n",
      "\tTrain Loss: 1.450 | Train PPL:   4.262\n",
      "\t Val. Loss: 1.655 |  Val. PPL:   5.234\n",
      "Epoch: 07 | Time: 0m 11s\n",
      "\tTrain Loss: 1.298 | Train PPL:   3.661\n",
      "\t Val. Loss: 1.631 |  Val. PPL:   5.111\n",
      "Epoch: 08 | Time: 0m 11s\n",
      "\tTrain Loss: 1.171 | Train PPL:   3.226\n",
      "\t Val. Loss: 1.623 |  Val. PPL:   5.069\n",
      "Epoch: 09 | Time: 0m 11s\n",
      "\tTrain Loss: 1.060 | Train PPL:   2.885\n",
      "\t Val. Loss: 1.633 |  Val. PPL:   5.119\n",
      "Epoch: 10 | Time: 0m 11s\n",
      "\tTrain Loss: 0.968 | Train PPL:   2.632\n",
      "\t Val. Loss: 1.637 |  Val. PPL:   5.140\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 10\n",
    "CLIP = 1\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    \n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss = train(model, train_iterator, optimizer, criterion, CLIP)\n",
    "    valid_loss = evaluate(model, valid_iterator, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "    \n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'tut6-model.pt')\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load our \"best\" parameters and manage to achieve a better test perplexity than all previous models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Test Loss: 1.682 | Test PPL:   5.377 |\n"
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('tut6-model.pt'))\n",
    "\n",
    "test_loss = evaluate(model, test_iterator, criterion)\n",
    "\n",
    "print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "\n",
    "Now we can can translations from our model with the `translate_sentence` function below.\n",
    "\n",
    "The steps taken are:\n",
    "- tokenize the source sentence if it has not been tokenized (is a string)\n",
    "- append the `<sos>` and `<eos>` tokens\n",
    "- numericalize the source sentence\n",
    "- convert it to a tensor and add a batch dimension\n",
    "- create the source sentence mask\n",
    "- feed the source sentence and mask into the encoder\n",
    "- create a list to hold the output sentence, initialized with an `<sos>` token\n",
    "- while we have not hit a maximum length\n",
    "  - convert the current output sentence prediction into a tensor with a batch dimension\n",
    "  - create a target sentence mask\n",
    "  - place the current output, encoder output and both masks into the decoder\n",
    "  - get next output token prediction from decoder along with attention\n",
    "  - add prediction to current output sentence prediction\n",
    "  - break if the prediction was an `<eos>` token\n",
    "- convert the output sentence from indexes to tokens\n",
    "- return the output sentence (with the `<sos>` token removed) and the attention from the last layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):\n",
    "    \n",
    "    model.eval()\n",
    "        \n",
    "    if isinstance(sentence, str):\n",
    "        nlp = spacy.load('de')\n",
    "        tokens = [token.text.lower() for token in nlp(sentence)]\n",
    "    else:\n",
    "        tokens = [token.lower() for token in sentence]\n",
    "\n",
    "    tokens = [src_field.init_token] + tokens + [src_field.eos_token]\n",
    "        \n",
    "    src_indexes = [src_field.vocab.stoi[token] for token in tokens]\n",
    "\n",
    "    src_tensor = torch.LongTensor(src_indexes).unsqueeze(0).to(device)\n",
    "    \n",
    "    src_mask = model.make_src_mask(src_tensor)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        enc_src = model.encoder(src_tensor, src_mask)\n",
    "\n",
    "    trg_indexes = [trg_field.vocab.stoi[trg_field.init_token]]\n",
    "\n",
    "    for i in range(max_len):\n",
    "\n",
    "        trg_tensor = torch.LongTensor(trg_indexes).unsqueeze(0).to(device)\n",
    "\n",
    "        trg_mask = model.make_trg_mask(trg_tensor)\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            output, attention = model.decoder(trg_tensor, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        pred_token = output.argmax(2)[:,-1].item()\n",
    "        \n",
    "        trg_indexes.append(pred_token)\n",
    "\n",
    "        if pred_token == trg_field.vocab.stoi[trg_field.eos_token]:\n",
    "            break\n",
    "    \n",
    "    trg_tokens = [trg_field.vocab.itos[i] for i in trg_indexes]\n",
    "    \n",
    "    return trg_tokens[1:], attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now define a function that displays the attention over the source sentence for each step of the decoding. As this model has 8 heads our model we can view the attention for each of the heads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_attention(sentence, translation, attention, n_heads = 8, n_rows = 4, n_cols = 2):\n",
    "    \n",
    "    assert n_rows * n_cols == n_heads\n",
    "    \n",
    "    fig = plt.figure(figsize=(15,25))\n",
    "    \n",
    "    for i in range(n_heads):\n",
    "        \n",
    "        ax = fig.add_subplot(n_rows, n_cols, i+1)\n",
    "        \n",
    "        _attention = attention.squeeze(0)[i].cpu().detach().numpy()\n",
    "\n",
    "        cax = ax.matshow(_attention, cmap='bone')\n",
    "\n",
    "        ax.tick_params(labelsize=12)\n",
    "        ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'], \n",
    "                           rotation=45)\n",
    "        ax.set_yticklabels(['']+translation)\n",
    "\n",
    "        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll get an example from the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['eine', 'frau', 'mit', 'einer', 'großen', 'geldbörse', 'geht', 'an', 'einem', 'tor', 'vorbei', '.']\n",
      "trg = ['a', 'woman', 'with', 'a', 'large', 'purse', 'is', 'walking', 'by', 'a', 'gate', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 8\n",
    "\n",
    "src = vars(train_data.examples[example_idx])['src']\n",
    "trg = vars(train_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our translation looks pretty good, although our model changes *is walking by* to *walks by*. The meaning is still the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'woman', 'with', 'a', 'large', 'purse', 'walks', 'by', 'a', 'gate', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the attention from each head below. Each is certainly different, but it's difficult (perhaps impossible) to reason about what head has actually learned to pay attention to. Some heads pay full attention to \"eine\" when translating \"a\", some don't at all, and some do a little. They all seem to follow the similar \"downward staircase\" pattern and the attention when outputting the last two tokens is equally spread over the final two tokens in the input sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's get an example the model has not been trained on from the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['ein', 'brauner', 'hund', 'rennt', 'dem', 'schwarzen', 'hund', 'hinterher', '.']\n",
      "trg = ['a', 'brown', 'dog', 'is', 'running', 'after', 'the', 'black', 'dog', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 6\n",
    "\n",
    "src = vars(valid_data.examples[example_idx])['src']\n",
    "trg = vars(valid_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model translates it by switching *is running* to just *running*, but it is an acceptable swap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'brown', 'dog', 'running', 'after', 'the', 'black', 'dog', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, some heads pay full attention to \"ein\" whilst some pay no attention to it. Again, most of the heads seem to spread their attention over both the period and `<eos>` tokens in the source sentence when outputting the period and `<eos>` sentence in the predicted target sentence, though some seem to pay attention to tokens from near the start of the sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll look at an example from the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien', '.']\n",
      "trg = ['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 10\n",
    "\n",
    "src = vars(test_data.examples[example_idx])['src']\n",
    "trg = vars(test_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A decent translation with *young* being omitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'mother', 'and', 'her', 'son', 'enjoying', 'a', 'beautiful', 'day', 'outside', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLEU\n",
    "\n",
    "Finally we calculate the BLEU score for the Transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchtext.data.metrics import bleu_score\n",
    "\n",
    "def calculate_bleu(data, src_field, trg_field, model, device, max_len = 50):\n",
    "    \n",
    "    trgs = []\n",
    "    pred_trgs = []\n",
    "    \n",
    "    for datum in data:\n",
    "        \n",
    "        src = vars(datum)['src']\n",
    "        trg = vars(datum)['trg']\n",
    "        \n",
    "        pred_trg, _ = translate_sentence(src, src_field, trg_field, model, device, max_len)\n",
    "        \n",
    "        #cut off <eos> token\n",
    "        pred_trg = pred_trg[:-1]\n",
    "        \n",
    "        pred_trgs.append(pred_trg)\n",
    "        trgs.append([trg])\n",
    "        \n",
    "    return bleu_score(pred_trgs, trgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a BLEU score of 35.08, which beats the 33.3 of the convolutional sequence-to-sequence model and 28.2 of the attention based RNN model. All this whilst having the least amount of parameters and the fastest training time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLEU score = 35.08\n"
     ]
    }
   ],
   "source": [
    "bleu_score = calculate_bleu(test_data, SRC, TRG, model, device)\n",
    "\n",
    "print(f'BLEU score = {bleu_score*100:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations for finishing these tutorials! I hope you've found them useful.\n",
    "\n",
    "If you find any mistakes or want to ask any questions about any of the code or explanations used, feel free to submit a GitHub issue and I will try to correct it ASAP. "
   ]
  }
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